Data Engineer at Float Infinity. I build quant-trading and machine-learning systems end to end: market-data pipelines, multi-factor models, backtests, and the tooling that runs them. Tested, observable, production-safe.
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PythonI'm a Data Engineer at Float Infinity (Sydney), where I build quant-trading and machine-learning systems end to end: real-time market-data pipelines, multi-factor models and backtests, with the full-stack React tooling that puts them in front of traders.
That production discipline runs through everything I ship: I've built a multi-venue derivatives trading platform (Nexus) and an AI integration layer for the Bloomberg Terminal, and presented my research at ICAI 2026. The throughline is the same: models and pipelines that are empirically justified, tested, and safe to run in production, not just notebooks that happened to backtest well.
I work with Float Infinity's Sydney team, and graduated from De La Salle University with a 3.83 GPA. I care more about whether a system holds up under real conditions than whether it looks clever on a slide.
Data engineering in production today, on top of a full-stack and quantitative track. The roles, and what each one delivered.
Developed an end to end NLP/ML quantitative trading algorithm across 15+ instruments, extracting 50+ engineered features across technical, sentiment, and macro signal layers, then built the Azure operational intelligence dashboards and ETL pipelines behind it.
Automated large scale data preprocessing and SQL ETL for over 1 million procurement and contract records, turning stale manual reporting into automated daily business intelligence stakeholders could act on.
Led the programming and creatives committee, directing technical initiatives, event development, and creative direction, alongside an executive role in finance and public relations.
Developed interactive Power BI and Excel dashboards for academic and small business clients, translating complex datasets into actionable insights through data cleaning, transformation, and exploratory analysis.
Bachelor of Science in Computer Science, Intelligent Systems track. A machine learning and data science foundation carried with distinction every year.
Seven-layer AI-powered derivatives system: multi-venue order book ingestion, VaR/Kelly risk controls, and live AI monitoring. Access-restricted.
Float InfinityFive projects, each broken down into problem, approach, stack, and measured outcome: the shape of every production write-up.
Real-time traffic sign detection app using computer vision for driver safety alerts.
Support vector machine-based face recognition system with image preprocessing.
Advanced ML workflow on adult income data with Optuna hyperparameter tuning.
Emotion response NLP program for sentiment and emotion classification.
Sentiment analysis pipeline on Twitter/X data with NLP techniques.
ML model predicting customer churn with XGBoost and feature engineering.
Data engineering first: ingestion, warehousing, and orchestration, plus the ML, DevOps, and interface tooling I reach for in production.
A focused list: agentic AI with LangGraph, Deep Learning Specialization, applied simulations, analytics tracks. Click a card to view the certificate.
Email is the fastest line. Response within 24 hours.
